In X-ray tomosynthesis, a series of low dose X-ray images are acquired over a range of X-ray beam orientations relative to an imaged object. Digital tomosynthesis (DTS) is a limited angle imaging technique, which allows the reconstruction of tomographic planes on the basis of the information contained within the images acquired during one tomographic image acquisition. More specifically, DTS is reconstruction of three-dimensional (3D) images from two-dimensional (2D) projection images of an object.
In DTS, one back-projection technique known as “simple back-projection” or the “shift and add algorithm” is often used to reconstruct 2D images into 3D images. This technique requires a relatively straightforward implementation and minimal computational power requirements. However, this technique introduces reconstruction artifacts. High contrast out-of-plane structures tend to appear as several relatively low-contrast copies in a reconstructed horizontal slice through the object. Also, the previously described loss in contrast for small structures is not recovered by the simple back-projection reconstruction technique. Thus, the conventional shift and add algorithm suffers from considerable problems in this field of use.
Another reconstruction method used in tomosynthesis is known as the algebraic reconstruction technique (ART). ART tends to generate higher quality reconstructions than the “shift and add algorithm,” but is typically much more computationally complex than other techniques (e.g., the shift and add algorithm). This computational cost and the associated delay until the final 3D image of the breast is available to the clinician can be prohibitive in practical clinical use.
Another reconstruction technique used in computed tomography (CT) imaging (i.e., filtered back-projection) utilizes projections over the full angular range (i.e., full 360 degree image acquisition about the object to be imaged) and a fine angular spacing between projections. Within this framework, filtered back-projection is a reconstruction method that yields high quality reconstructions with few artifacts. Unfortunately, full 360 degree image acquisition is not practical for many applications including breast imaging, where design considerations limit the ability to rotate fully about the breast.
X-ray imaging systems are desirable in comparison to other imaging systems because X-ray imaging is a relatively low cost technique that uses relatively low doses of radiation. However, conventional X-ray imaging systems do not properly visually distinguish lung nodules in the X-ray image to the extent that 30% of nodules are medically diagnosed; 70% of lung nodules are not medically diagnosed.
There are two primary reasons for the inadequate imaging of X-ray imaging systems. The first reason is overlapping anatomic structures that constitute anatomic artifacts that cause anatomic imaging noise. The anatomic structures that obscure nodules in the image are spine, heart, muscles, shoulder bones, and artificial heart. The density of the anatomic structures creates a very bright section in the X-ray image. Lung nodules are less dense than these anatomic structures, making a lung nodule difficult to visually distinguish in the X-ray image. In an analogy, X-ray imaging is similar to locating a bird in a forest. If the view of the bird is obscured by the trees and leaves, the bird will be nearly impossible to visually identify in the forest. Similarly, if the view of a lung nodule is obscured by ribs, the lung nodule will be nearly impossible to visually identify in the body.
The second reason for the inadequate imaging of X-ray imaging systems is that conventional X-ray images are inherently somewhat blurred. To complicate matters, in the early stages of development lung nodules, the primary distinguishing characteristic of the nodules are that the nodules contain slightly more fluid that surrounding tissue, making the contrast between the lung nodule and the surrounding tissue in the X-ray image rather slight. The slight visual contrast may be very difficult to visually pick out in the image that is somewhat blurred.
Conventional reconstruction algorithms focus mainly on removing the underlying/overlying structures, seldom taking into account the unique characteristics of tomosynthesis projections, especially its spectrum, and optimizing the algorithm to enhance the structures of interested (SOI). Conventional filtered back-projection geometrically transforms the tomosynthesis projections to a form suitable for CT/VCT reconstruction. But these algorithms are known to produce “streaking artifacts.” Conventional filtered back-projection is able to successfully enhance the contrast of SOIs of certain sizes, but suffers from artifacts caused by aliasing and the complexity to optimize the algorithm for all SOIs present in general X-ray radiography images.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for an improved filtering technique for two-dimensional images. In particular, there is a need in the art for improved visual distinction of overlapping anatomic structures in X-ray images and to reduce blurring in X-ray images. There is also a need for an improved technique of processing two-dimensional images into reconstructed three-dimensional images.